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• A FE model calibration based on AVT and machine learning process is presented. • The dynamic parameters of the investigated tower are estimated via OMA techniques. • Different parameterization and modelling assumptions are employed for the updating. • The calibrated model accurately reproduces the real dynamic behaviour of the tower. Cultural Heritage preservation requires the combination of in situ investigations and accurate Finite Elements models in order to correctly interpret the empirical evidence and successfully apply advanced structural analyses for health assessment purposes, allowing to infer about the future evolution of the structural response and timely detect deviations from the expected behaviour. In this paper the actual dynamic behaviour of the Civic Tower of Ostra, Italy, is thoroughly investigated by means of a detailed numerical model built and calibrated using the experimental modal features estimated through field dynamic testing. To this end, a fully automated Finite Element Model Updating procedure based on genetic algorithms and machine learning is conceived and employed, allowing the successful estimation of the unknown material properties of the tower, considering both isotropic and orthotropic behavioural models for masonry. The results enabled to establish baseline information on the current structural condition of the heritage and to set performance standards that will serve to optimise the control of the structural integrity over time. [ABSTRACT FROM AUTHOR]